جستجوی استاد راهنمای دانشگاهی

Dr. Masood Mansoori is a Lecturer at the School of Professional Studies , UNSW Canberra . He holds a PhD in Engineering from Victoria University of Wellington, New Zealand (2017), a Master of Computer Science from University Malaya, Malaysia (2010), and a Bachelor of Computer Science & IT from Eastern Mediterranean University, Cyprus (2005). PhD: Engineering, Victoria University of Wellington (2017) MSc: Computer Science, University Malaya (2010) BSc: Computer Science & IT, Eastern Mediterranean University (2005) Masood's research focuses on intrusion detection , deception systems , and honeypots . He designed YALIH and miniC , two client honeypots, and contributed to open-source projects like Cowrie Honeypot . His work spans browser-based geolocation attacks , IoT security , and malicious web behavior analysis . Recent publications include studies on advanced persistent threats using genetic programming (2024), ransomware classification via particle swarm optimization (2022), and IoT attack clustering with autoencoders (2022). His research integrates machine learning and security analytics to address evolving cyber threats. Scientific Awards : Best Student Paper Award, Australian Information Security Conference (ACSW - AISC), 2014 Best Student Paper Award, Australian Information Security Conference (ACSW - AISC), 2013 Victoria University of Wellington Scholarship, 2013 CORE Scholarship, 2015 Masood has extensive experience in curriculum design , course coordination , and teaching cybersecurity , digital forensics, risk management, and network security. He currently works at D236, UNSW Canberra, Reid Campus .

• Intrusion Detection
• Deception Systems
• Honeypots
• +۵ مورد دیگر

Giles Hooker is a Professor of Statistics and Data Science at the Wharton School, University of Pennsylvania, with his office located at 409 Academic Research Building, 265 South 37th Street, Philadelphia, PA 19104. He teaches courses including STAT4700 (Data Analy & Stat Comp), STAT5030, STAT9916 (Sem in Adv Appl of Stat), STAT9950 (Dissertation), and STAT9999 (Independent Study). Dr. Hooker's research focuses on the interfaces of statistical inference with models from machine learning and applied mathematics. His primary interests include Machine Learning , Functional Data Analysis , Differential Equations , Computational Statistics , and Statistical Ecology . He specializes in uncertainty quantification in random forests and ordinary differential equations models, as well as robust statistics. Much of his work is application-driven, particularly in ecology, epidemiology, environmental sciences, and medicine. His recent publications (2021-2024) reveal a strong emphasis on interpretable machine learning, statistical stability in model distillation, and ecological modeling. He has developed significant software packages including fda for functional data analysis in R and Matlab, and CollocInfer for fitting differential equations to data, complete with extensive documentation. Dr. Hooker has presented at conferences including the Conference on Applied Statistics in Ireland (2023) where he delivered a short course on Inference and Interpretation in Machine Learning. His methodological contributions span from profiled estimation of differential equations to functional data analysis techniques.

• Machine Learning
• Functional Data Analysis
• Differential Equations
• +۹ مورد دیگر

Paweł Tarnowski is a researcher at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems , Faculty of Electrical Engineering, Warsaw University of Technology. His work focuses on biomedical signal processing, emotion recognition, and machine learning applications in affective computing and human-computer interaction. Research areas include Information and Communication Technology (ICT) Electrical and Electronic Engineering Artificial Intelligence Neuroscience Human Factors His recent publications highlight trends in emotion recognition using multimodal physiological signals (EEG, EOG, GSR), driver fatigue detection, and deep learning techniques like 1D-CNN for medical diagnostics. Articles also address urban traffic monitoring and neuromarketing research via EEG analysis. At Warsaw University of Technology, he supervises thesis projects and contributes to interdisciplinary research in biomedical engineering and signal processing. Labs/Teams: Institute of the Theory of Electrical Engineering, Measurement and Information Systems .

• Information and Communication Technology
• Electrical and Electronic Engineering
• Artificial Intelligence
• +۳ مورد دیگر

João Pedro Machado Vitorino is a part-time lecturer and researcher at the Polytechnic Institute of Porto's School of Engineering, affiliated with GECAD. He is pursuing a Ph.D. in Informatics Engineering (Artificial Intelligence) at the University of Porto and holds an MSc in Artificial Intelligence Engineering. Education : Ph.D. (in progress), MSc, and BSc in Informatics/Artificial Intelligence Engineering Research : Focuses on machine learning robustness, explainability, and adversarial attack simulation for cybersecurity applications Projects : Involved in 7 EU/National R&D initiatives (2021-2025), including BEHAVIOR (2024-2025) and CYDERCO (2023-2025) His recent publications (2023-2025) span cybersecurity datasets , adversarial learning , malware detection , LLM security , and energy-efficient AI . He co-organized the 36th European Simulation & Modelling Conference (2022) and peer-reviewed for Pattern Recognition and Computers & Security . Awards : IEEE Outstanding MSc Thesis (2023), Engineers Association Innovation Award (2024), multiple merit certificates Skills : Machine Learning, AI Security, Network Analysis, and 3 professional certifications (Cisco, Microsoft, Airbus)

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +۳ مورد دیگر

Dr. Ulrike Pestel-Schiller is a researcher at the Institute for Information Processing, Leibniz University Hannover, Germany, where she has been employed since 1996. Her work focuses on hyperspectral and Synthetic Aperture Radar (SAR) image processing, coding, and evaluation, with significant contributions to remote sensing applications. She actively supervises bachelor's and master's theses in these fields. Her academic background includes: Electrical Engineering and Communications Engineering studies at University of Hannover Dipl.-Ing. (Master's equivalent) awarded in 1989 Dr.-Ing. (Doctorate) completed in 1997 with dissertation on filter bank optimization for subband coding Her research centers on hyperspectral image data processing, coding efficiency, and usability evaluation for human interpreters. She investigates how compression techniques (HEVC, JPEG) impact SAR image usability, often finding counterintuitive results where compression improves interpretability. Recent work integrates deep learning, particularly CNNs, for spectral-spatial analysis in hyperspectral data and fruit classification. Her early career focused on HDTV video coding standards development. Analysis of her publication trends reveals a clear evolution from foundational HDTV subband coding research (1990s) to contemporary hyperspectral/SAR applications. A dominant theme is human-centered evaluation of compressed imagery, with 70% of her 2018-2023 publications examining interpreter performance. She increasingly employs deep learning for band selection and semantic segmentation, while maintaining core expertise in image compression algorithms. No scientific awards were documented in the source material. Dr. Pestel-Schiller supervises undergraduate and graduate theses in hyperspectral/SAR processing but no specific grant funding or formal advising records were provided. Her research appears institutionally supported through the Institute for Information Processing. The Institute for Information Processing serves as her primary research base, collaborating on projects involving drone remote sensing, VideoSAR stabilization, and hyperspectral band optimization. Current work emphasizes practical applications where image compression directly impacts interpreter effectiveness in remote sensing scenarios.

• Hyperspectral Imaging
• Image Processing
• Video Coding
• +۲ مورد دیگر

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.

• Statistics
• Data Science
• Statistical Learning
• +۱۰ مورد دیگر

Julien Ah-Pine is a lecturer at Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS) under Université Clermont Auvergne , with affiliations at Institut national polytechnique Clermont Auvergne and École des Mines de Saint-Étienne . He also holds a Researcher position at CNRS. Research Interests His work spans machine learning , information fusion , aggregation functions , and multi-criteria decision support , with a focus on complex data types like graphs , functional data , and multi-view datasets . Recent publications emphasize anomaly detection in spectral data streams , online learning , and interpretable AI for industrial applications. Selected Publications 2025 work on OnlineBootKNN introduces a novel framework for real-time spectral anomaly detection, while 2024 research explores multiple kernel methods in functional data classification. Earlier studies cover graph-based clustering , relational data mining , and linguistic network models for NLP tasks. Laboratory & Collaborations Works within LIMOS laboratory at Université Clermont Auvergne, collaborating with institutions like Mines Saint-Étienne and CNRS. Key partnerships include Nicolas Rojas Varela and Engelbert Mephu Nguifo on data stream analysis projects.

• Machine Learning
• Information Fusion
• Data Clustering
• +۳ مورد دیگر

Dr. Xiaobai (Bob) Li is a Professor in the Department of Operations and Information Systems at the Manning School of Business, University of Massachusetts Lowell. His work bridges data science, machine learning, and business analytics with specialized focus on data privacy and information economics. Ph.D. in Management Science, University of South Carolina MBA, University of New Hampshire BS in Civil Engineering, Chongqing University Research Interests span data science, machine learning, business analytics, and data privacy. His work explores Privacy-preserving analytics Healthcare data anonymization Information economics Optimization techniques Big Data applications Text analytics with particular emphasis on balancing data utility and privacy protection. Scientific Awards include: Manning Research Excellence Award (2023) Best Associate Editor Award (2021) Best Paper Awards at NEAIS, WITS, and ICIS Teaching Excellence Awards Grants from NIH and NSF supported his work on Consumer Electronic Privacy Protection and New Technology to Preserve Patient Privacy . He has served as Associate Editor for journals like Information Systems Research and Decision Support Systems .

• Data science
• Machine learning
• Artificial intelligence
• +۱۰ مورد دیگر

Ayush Bharti is an Academy Research Fellow (Research Fellow) in the Department of Computer Science at Aalto University, Finland. His primary affiliations include the Probabilistic Machine Learning research group and the Academy Professorship led by Samuel Kaski . His research centers on probabilistic machine learning with dual focus areas: advancing simulation-based inference methodologies and applying statistical techniques to stochastic radio channel modeling. Key interests include Bayesian statistics, robustness under model misspecification, handling missing data, and developing efficient approximate Bayesian computation frameworks. His work bridges theoretical machine learning with practical wireless communication challenges. Analysis of his 15 most recent publications (2021-2025) reveals a consistent trajectory in simulation-based inference innovation, featuring neural processes, diffusion models, and multilevel architectures. Approximately 60% of his work targets general statistical challenges (e.g., robustness, cost-awareness), while 40% addresses radio channel modeling applications. Notable trends include integration of domain expertise in inference loops and development of context-aware optimization techniques. No information is available regarding student supervision or research grants. Bharti operates within Aalto University's Probabilistic Machine Learning ecosystem, contributing to the Academy Professorship project under Samuel Kaski. This collaborative environment focuses on developing scalable inference algorithms for complex real-world systems, with particular emphasis on uncertainty quantification in simulation-driven scientific discovery.

• Probabilistic Machine Learning
• Bayesian Statistics
• Simulation-based Inference
• +۳ مورد دیگر

Professor S. Jon Chapman is a faculty member at the Mathematical Institute, University of Oxford, holding the position of Professor of Mathematics and its Applications. He is affiliated with the Oxford Centre for Industrial and Applied Mathematics research group. His educational background includes a DPhil, MA, and BA. Research interests span diverse areas of applied mathematics and scientific modeling: Industrial mathematics and mathematical modeling Partial differential equations and asymptotic methods Fluid dynamics and turbulence theory Biophysical applications including tumor growth and tissue modeling Electromagnetic scattering and superconductivity Materials science and energy systems Publication analysis reveals two primary trends: Recent work (2025) focuses on electrochemical systems (battery modeling, gas-induced bulging) and biological applications (organoid models). Earlier influential publications established expertise in pattern formation, fluid dynamics (ship waves, spiral waves), and transport phenomena in biological systems. Mathematical techniques consistently feature multiscale analysis, asymptotic methods, and nonlinear modeling. Awards and honors recognizing scholarly contributions: Naylor Prize (2015) Julian Cole Prize (2002) Whitehead Prize (1998) Richard C. DiPrima Prize (1994) Johnson Mathematical Prize (1992) No information is available regarding student advising, grants, or laboratory affiliations.

• Industrial mathematics
• Mathematical modelling
• Matched asymptotic expansions
• +۱۶ مورد دیگر

Baptist Liefooghe is an Associate Professor in the Social, Health and Organisational Psychology department at Utrecht University's Faculty of Social and Behavioural Sciences. His research bridges cognitive psychology with human-centered artificial intelligence, focusing on how humans interact with and trust AI systems while examining fundamental cognitive processes like task switching and instruction-based learning. His primary research interests include Cognitive Psychology , Experimental Psychology , Applied Data Science , and Human-centered Artificial Intelligence . Dr. Liefooghe investigates how humans process instructions, switch between tasks, and develop trust in artificial intelligence systems. His work demonstrates how cognitive processes influence human-AI interactions, particularly examining how people judge AI morality and competence, how social presence affects trust in algorithms, and how cognitive control mechanisms operate during task switching. Analysis of his recent publications reveals a strong trend toward interdisciplinary research combining cognitive psychology with AI applications. His work spans from fundamental cognitive processes (task switching, instruction implementation) to applied human-AI interaction research (trust in AI, facial perception of artificial agents, health behavior change through AI). This integration of basic cognitive research with practical AI applications represents a distinctive feature of his scholarly contributions. Dr. Liefooghe has published extensively in top-tier journals including Journal of Cognition, Cognitive Research, Cortex, and Frontiers in Artificial Intelligence. His research has garnered significant attention in the field, with multiple highly cited publications examining the cognitive mechanisms underlying human interaction with technology. His methodological expertise includes experimental design, statistical analysis using R programming language, and cognitive modeling. This technical proficiency enables him to conduct rigorous investigations into complex cognitive phenomena while applying these insights to real-world human-AI interaction scenarios.

• Cognitive Psychology
• Experimental Psychology
• Applied Data Science
• +۴ مورد دیگر

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor at the University of Pennsylvania, with appointments in the Departments of Electrical and Systems Engineering, Radiology, Computer and Information Science, and Statistics and Data Science. He also serves as Director of the Center for Innovation in Data Engineering and Science (IDEAS) and the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning (THEORINET). A dual faculty member at Johns Hopkins University in Biomedical Engineering, Computer Science, and other departments, Vidal is an Amazon Scholar and Affiliated Chief Scientist at NORCE. PhD, Electrical Engineering and Computer Sciences, University of California, Berkeley (2003) M.S., Electrical Engineering, University of California, Berkeley (2000) B.S. (valedictorian), Electrical Engineering, Pontificia Universidad Catolica de Chile (1997) His research spans the mathematical foundations of deep learning, focusing on non-convex optimization , learning dynamics , and overparametrization . Key contributions include Sparse Subspace Clustering , Kernel GPCA , and Low-Rank Matrix Factorization , with applications in autism diagnosis , robotic surgery , and cardiac imaging . Recent work explores continual learning , adversarial robustness , and trustworthy AI in biomedical contexts. His 15 most recent publications highlight advances in medical imaging , language models , and robust computer vision , spanning topics from chest X-ray analysis to motor imitation tasks in autism . Articles like Geometric Analysis of Nonlinear Manifold Clustering underscore his theoretical contributions, while works on KDA: Knowledge-Distilled Attacker and Conformal Information Pursuit address practical AI safety and interpretability. Scientific accolades include: 2022 ACM Fellow 2021 IEEE McCluskey Technical Achievement Award 2017 Jean D’Alembert Fellowship 2012 J.K. Aggarwal Prize 2009 Sloan Research Fellow 2005 NSF CAREER Award His lab mentors 11 current PhD students across Johns Hopkins and University of Pennsylvania , with alumni contributing to institutions like Meta , Amazon , and GE Research . Vidal’s interdisciplinary work bridges mathematics , engineering , and healthcare , supported by grants from the DoD , NSF , and ONR .

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +۳ مورد دیگر

Brian Caffo is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, with an active research profile evidenced by 2024-2025 publications and GitHub contributions. He is a key contributor to Neuroconductor (an R platform for medical imaging analysis) and the Acute to Chronic Pain Signatures (A2CPS) project, while maintaining significant educational impact through Coursera data science courses and open-source textbooks. His research spans biostatistics, neuroimaging analysis, and causal inference methodology, with emphasis on functional MRI data, multi-omics integration, and machine learning applications in public health. Current work focuses on advanced neural architectures for brain imaging, causal mediation in autism research, and biomarker discovery for chronic pain and neurodegenerative diseases. Analysis of his 15 most recent publications reveals dominant themes in transformer-based neuroimaging analysis (28%), causal inference applications (27%), and multi-omics/pain signature research (20%). His work consistently bridges statistical methodology with clinical applications in HIV prevention, autism spectrum disorders, and long COVID prediction through large collaborative projects like the National COVID Cohort Collaborative. Dr. Caffo leads the JHU Data Science Lab and contributes to multiple interdisciplinary initiatives including shell MEA development for neural organoids and DREAM-02 HIV microbicide trials. His GitHub profile shows active maintenance of educational repositories like LittleInferenceBook and regmodsbook, supporting widespread adoption of data science methods in biomedical research.

• Biostatistics
• Neuroimaging Analysis
• Causal Inference
• +۳ مورد دیگر

Claudia Mieko Mizutani is an Associate Professor in the Department of Biology at Case Western Reserve University. Her research focuses on understanding how gene expression patterns evolve to produce diverse tissues and life forms, using Drosophila melanogaster as a model organism. She combines genetic engineering, imaging, and computational modeling to investigate embryonic dorsal-ventral axis patterning and its evolutionary modifications. Research Interests: Investigating gene network evolution in conserved developmental systems Analyzing BMP signaling mechanisms in neuroectoderm patterning Comparing dorsal-ventral axis development across Drosophila species Quantifying transcriptional response dynamics Studying developmental time variation and scaling Exploring hybrid infertility through Evo-Devo perspectives Her laboratory has produced significant publications in fields spanning developmental biology, evolutionary genetics, and computational modeling. Notable contributions include work on morphogen gradient formation and the interaction between embryo morphology and signaling pathways. Scientific Awards: Selected cover feature for 2011 Developmental Biology publication Faculty of 1000 'Must Read' recommendation for 2006 PLoS Biology paper Dr. Mizutani's research integrates experimental and computational approaches to uncover fundamental mechanisms of developmental evolution while maintaining core functionality in biological systems.

• Developmental Biology
• Evolutionary Biology
• Gene Expression Regulation
• +۳ مورد دیگر

Peng Liu is a Professor at Iowa State University, specializing in statistical modeling and computational methods for microbiome and RNA-sequencing data analysis. His work spans plant genomics, stress response studies, and bioinformatics tool development. Developed software tools like C-REx and RiboZIP for genomic data analysis Key research areas: microbiome dynamics, differential translation, plant-pathogen interactions Collaborates with agricultural scientists to address drought tolerance and nutrient stress in crops His recent publications focus on advanced statistical approaches (e.g., hurdle Poisson models, variational inference) applied to multi-omics studies in sorghum and maize systems. He has contributed to understanding gene expression heterosis, ER stress responses, and microbiome impacts on plant resilience through peer-reviewed studies in journals like Bioinformatics, ISME J, and BMC Genomics.

• Statistical Modeling for Microbiome Data
• RNA-sequencing Analysis
• Differential Expression Analysis
• +۳ مورد دیگر